Face detection is a demanding task for many reasons. In addition to actual face detection, that is, detecting that there is a face is in the image, one may want to identify the face so as to link the identified face to a specific identity (a specific person). Such a face recognition is used in a wide variety of applications including security systems, biometric detection, authentication, generation of metadata from a plurality of images and measuring the similarity of faces, to mention only a few.
Face recognition can be seen as a specific case of object detection. However, face recognition is significantly more challenging than simple object detection because the characteristics of a face are more detailed than the characteristics of a simple object, such as a cubic box, for example. Further, the appearance of face changes in time and is also deformable in shape, and is thus more challenging to identify than man-made objects. Neighboring pixels have therefore different dependency on each other than in general object detection. Further, general object detection does not require the interpretation of the identity of the object.
In face recognition facial features are typically extracted from the image and compared to a predetermined feature database. Current face recognition technologies apply, for example, local binary patterns (LBP), where information is obtained by comparing one location of an image (one pixel) to one neighboring pixel. This approach as such has disadvantages related to lack of discriminating facial features leading to poor accuracy and poor consideration of a local structure of neighboring pixels. In addition, different approaches than LBP, namely a local phase quantization (LPQ) technique or a decorrelated LPQ technique has been adopted to face recognition. However, these solutions for performing face recognition include also several disadvantages related to, for example, excessive amount of features and data leading to slow processing compared to LBP. Moreover, the use of LPQ does not provide as accurate results as the use of LBP. Further, these solutions as such have problems with uneven illumination of the face and varying viewpoint towards the face, for example.
Accordingly, a novel solution for performing face recognition is needed.